An introduction to signal detection and estimation (2nd ed.)
An introduction to signal detection and estimation (2nd ed.)
Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Introduction to Algorithms
Dynamic Power Management in Wireless Sensor Networks
IEEE Design & Test
Maximum lifetime routing in wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Towards optimal sleep scheduling in sensor networks for rare-event detection
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Dynamic wake-up and topology maintenance protocols with spatiotemporal guarantees
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Power allocation in distributed detection with wireless sensor networks
MILCOM'06 Proceedings of the 2006 IEEE conference on Military communications
Quantizer Precision for Distributed Estimation in a Large Sensor Network
IEEE Transactions on Signal Processing
Detection in Sensor Networks: The Saddlepoint Approximation
IEEE Transactions on Signal Processing
Type-Based Decentralized Detection in Wireless Sensor Networks
IEEE Transactions on Signal Processing
Cross-Layer Design of Sequential Detectors in Sensor Networks
IEEE Transactions on Signal Processing
Decentralized Inference Over Multiple-Access Channels
IEEE Transactions on Signal Processing - Part I
Nonparametric decentralized detection using kernel methods
IEEE Transactions on Signal Processing
Decentralized detection in sensor networks
IEEE Transactions on Signal Processing
Asymptotic Performance of a Censoring Sensor Network
IEEE Transactions on Information Theory
IEEE Communications Magazine
Asymptotic results for decentralized detection in power constrained wireless sensor networks
IEEE Journal on Selected Areas in Communications
Energy-efficient detection in sensor networks
IEEE Journal on Selected Areas in Communications
Signal recovery with cost-constrained measurements
IEEE Transactions on Signal Processing
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We consider the problem of sensor selection in a heterogeneous sensor network when several types of binary sensors with different discrimination performance and costs are available. We want to analyze what is the optimal proportion of sensors of each class in a target detection problem when a total cost constraint is specified. We obtain the conditional distributions of the observations at the fusion center given the hypotheses, necessary to perform an optimal hypothesis test in this heterogeneous scenario. We characterize the performance of the tests by means of the symmetric KuUback-Leibler divergence, or J-divergence, applied to the conditional distributions under each hypothesis. By formulating the sensor selection as a constrained maximization problem, and showing the linearity of the J-divergence with the number of sensors of each class, we found that the optimal proportion of sensors is "winner takes all" like. The sensor class with the best performance/cost ratio is selected.